Overview

Dataset statistics

Number of variables35
Number of observations934858
Missing cells366522
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory256.8 MiB
Average record size in memory288.0 B

Variable types

Numeric21
Categorical14

Alerts

device_fraud_count has constant value ""Constant
prev_address_months_count is highly overall correlated with current_address_months_countHigh correlation
current_address_months_count is highly overall correlated with prev_address_months_countHigh correlation
customer_age is highly overall correlated with date_of_birth_distinct_emails_4wHigh correlation
velocity_24h is highly overall correlated with velocity_4w and 1 other fieldsHigh correlation
velocity_4w is highly overall correlated with velocity_24h and 1 other fieldsHigh correlation
bank_branch_count_8w is highly overall correlated with bank_months_countHigh correlation
credit_risk_score is highly overall correlated with proposed_credit_limitHigh correlation
bank_months_count is highly overall correlated with bank_branch_count_8wHigh correlation
proposed_credit_limit is highly overall correlated with credit_risk_scoreHigh correlation
month is highly overall correlated with velocity_24h and 1 other fieldsHigh correlation
date_of_birth_distinct_emails_4w is highly overall correlated with customer_ageHigh correlation
fraud_bool is highly imbalanced (91.3%)Imbalance
foreign_request is highly imbalanced (83.7%)Imbalance
source is highly imbalanced (93.5%)Imbalance
device_distinct_emails_8w is highly imbalanced (87.4%)Imbalance
name_email_similarity has 61087 (6.5%) missing valuesMissing
date_of_birth_distinct_emails_4w has 61087 (6.5%) missing valuesMissing
email_is_free has 61087 (6.5%) missing valuesMissing
device_distinct_emails_8w has 61087 (6.5%) missing valuesMissing
phone_home_valid has 61087 (6.5%) missing valuesMissing
phone_mobile_valid has 61087 (6.5%) missing valuesMissing
id is uniformly distributedUniform
id has unique valuesUnique
x1 has unique valuesUnique
x2 has unique valuesUnique
bank_branch_count_8w has 132507 (14.2%) zerosZeros
month has 74895 (8.0%) zerosZeros

Reproduction

Analysis started2023-05-30 17:25:29.937416
Analysis finished2023-05-30 17:29:31.804451
Duration4 minutes and 1.87 second
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct934858
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500013.56
Minimum0
Maximum999999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2023-05-30T13:29:31.931289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49990.85
Q1250014.25
median500128.5
Q3749857.75
95-th percentile950057.15
Maximum999999
Range999999
Interquartile range (IQR)499843.5

Descriptive statistics

Standard deviation288644.63
Coefficient of variation (CV)0.57727361
Kurtosis-1.1997399
Mean500013.56
Median Absolute Deviation (MAD)249923
Skewness-0.00026112347
Sum4.6744168 × 1011
Variance8.3315724 × 1010
MonotonicityStrictly increasing
2023-05-30T13:29:32.105809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
666623 1
 
< 0.1%
666626 1
 
< 0.1%
666627 1
 
< 0.1%
666628 1
 
< 0.1%
666629 1
 
< 0.1%
666630 1
 
< 0.1%
666632 1
 
< 0.1%
666633 1
 
< 0.1%
666634 1
 
< 0.1%
Other values (934848) 934848
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
999999 1
< 0.1%
999998 1
< 0.1%
999997 1
< 0.1%
999996 1
< 0.1%
999995 1
< 0.1%
999994 1
< 0.1%
999993 1
< 0.1%
999992 1
< 0.1%
999991 1
< 0.1%
999990 1
< 0.1%

fraud_bool
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
0.0
924585 
1.0
 
10273

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2804574
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 924585
98.9%
1.0 10273
 
1.1%

Length

2023-05-30T13:29:32.241719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-30T13:29:32.366163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 924585
98.9%
1.0 10273
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 1859443
66.3%
. 934858
33.3%
1 10273
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1869716
66.7%
Other Punctuation 934858
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1859443
99.5%
1 10273
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 934858
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2804574
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1859443
66.3%
. 934858
33.3%
1 10273
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2804574
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1859443
66.3%
. 934858
33.3%
1 10273
 
0.4%

income
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57867516
Minimum0.1
Maximum0.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2023-05-30T13:29:32.454130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.3
median0.6
Q30.8
95-th percentile0.9
Maximum0.9
Range0.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.28824805
Coefficient of variation (CV)0.4981172
Kurtosis-1.2206216
Mean0.57867516
Median Absolute Deviation (MAD)0.2
Skewness-0.46550402
Sum540979.1
Variance0.083086937
MonotonicityNot monotonic
2023-05-30T13:29:32.560573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.9 225216
24.1%
0.8 141589
15.1%
0.1 135633
14.5%
0.6 103016
11.0%
0.7 98145
10.5%
0.4 73358
 
7.8%
0.2 60722
 
6.5%
0.5 51489
 
5.5%
0.3 45690
 
4.9%
ValueCountFrequency (%)
0.1 135633
14.5%
0.2 60722
 
6.5%
0.3 45690
 
4.9%
0.4 73358
 
7.8%
0.5 51489
 
5.5%
0.6 103016
11.0%
0.7 98145
10.5%
0.8 141589
15.1%
0.9 225216
24.1%
ValueCountFrequency (%)
0.9 225216
24.1%
0.8 141589
15.1%
0.7 98145
10.5%
0.6 103016
11.0%
0.5 51489
 
5.5%
0.4 73358
 
7.8%
0.3 45690
 
4.9%
0.2 60722
 
6.5%
0.1 135633
14.5%

prev_address_months_count
Real number (ℝ)

Distinct373
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.674602
Minimum-1
Maximum399
Zeros0
Zeros (%)0.0%
Negative712414
Negative (%)76.2%
Memory size14.3 MiB
2023-05-30T13:29:32.707096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile96
Maximum399
Range400
Interquartile range (IQR)0

Descriptive statistics

Standard deviation43.044012
Coefficient of variation (CV)2.933232
Kurtosis22.352884
Mean14.674602
Median Absolute Deviation (MAD)0
Skewness4.3194346
Sum13718669
Variance1852.787
MonotonicityNot monotonic
2023-05-30T13:29:32.876993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 712414
76.2%
11 8730
 
0.9%
29 7750
 
0.8%
28 7489
 
0.8%
27 7343
 
0.8%
30 7279
 
0.8%
10 7022
 
0.8%
12 6663
 
0.7%
31 6660
 
0.7%
26 6503
 
0.7%
Other values (363) 157005
 
16.8%
ValueCountFrequency (%)
-1 712414
76.2%
6 26
 
< 0.1%
7 279
 
< 0.1%
8 1217
 
0.1%
9 3703
 
0.4%
10 7022
 
0.8%
11 8730
 
0.9%
12 6663
 
0.7%
13 3499
 
0.4%
14 1195
 
0.1%
ValueCountFrequency (%)
399 1
 
< 0.1%
386 1
 
< 0.1%
382 1
 
< 0.1%
376 1
 
< 0.1%
373 1
 
< 0.1%
372 2
 
< 0.1%
371 3
< 0.1%
370 4
< 0.1%
369 3
< 0.1%
368 7
< 0.1%
Distinct416
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.223237
Minimum-1
Maximum429
Zeros7261
Zeros (%)0.8%
Negative3228
Negative (%)0.3%
Memory size14.3 MiB
2023-05-30T13:29:33.052336image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile4
Q127
median64
Q3154
95-th percentile303
Maximum429
Range430
Interquartile range (IQR)127

Descriptive statistics

Standard deviation94.061999
Coefficient of variation (CV)0.94798357
Kurtosis0.67141507
Mean99.223237
Median Absolute Deviation (MAD)52
Skewness1.1722444
Sum92759637
Variance8847.6596
MonotonicityNot monotonic
2023-05-30T13:29:33.192930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 12828
 
1.4%
6 12702
 
1.4%
8 12702
 
1.4%
9 12398
 
1.3%
5 12093
 
1.3%
10 11794
 
1.3%
4 11499
 
1.2%
11 11137
 
1.2%
3 10629
 
1.1%
12 10249
 
1.1%
Other values (406) 816827
87.4%
ValueCountFrequency (%)
-1 3228
 
0.3%
0 7261
0.8%
1 8462
0.9%
2 9792
1.0%
3 10629
1.1%
4 11499
1.2%
5 12093
1.3%
6 12702
1.4%
7 12828
1.4%
8 12702
1.4%
ValueCountFrequency (%)
429 1
 
< 0.1%
423 2
< 0.1%
413 2
< 0.1%
412 2
< 0.1%
411 1
 
< 0.1%
410 2
< 0.1%
409 1
 
< 0.1%
408 3
< 0.1%
406 1
 
< 0.1%
405 4
< 0.1%

customer_age
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.343605
Minimum10
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2023-05-30T13:29:33.316422image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile20
Q130
median50
Q350
95-th percentile60
Maximum90
Range80
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.765578
Coefficient of variation (CV)0.33295544
Kurtosis-0.68118307
Mean41.343605
Median Absolute Deviation (MAD)10
Skewness-0.1956127
Sum38650400
Variance189.49115
MonotonicityNot monotonic
2023-05-30T13:29:33.433495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
50 358162
38.3%
30 176282
18.9%
20 137357
 
14.7%
40 136268
 
14.6%
60 93271
 
10.0%
70 17462
 
1.9%
10 12138
 
1.3%
80 3662
 
0.4%
90 256
 
< 0.1%
ValueCountFrequency (%)
10 12138
 
1.3%
20 137357
 
14.7%
30 176282
18.9%
40 136268
 
14.6%
50 358162
38.3%
60 93271
 
10.0%
70 17462
 
1.9%
80 3662
 
0.4%
90 256
 
< 0.1%
ValueCountFrequency (%)
90 256
 
< 0.1%
80 3662
 
0.4%
70 17462
 
1.9%
60 93271
 
10.0%
50 358162
38.3%
40 136268
 
14.6%
30 176282
18.9%
20 137357
 
14.7%
10 12138
 
1.3%

days_since_request
Real number (ℝ)

Distinct925125
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.90384319
Minimum3.1127908 × 10-8
Maximum76.577505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2023-05-30T13:29:33.619843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3.1127908 × 10-8
5-th percentile0.0014623427
Q10.0074449506
median0.015668693
Q30.026927433
95-th percentile3.6445101
Maximum76.577505
Range76.577505
Interquartile range (IQR)0.019482483

Descriptive statistics

Standard deviation5.0068073
Coefficient of variation (CV)5.5394645
Kurtosis123.72027
Mean0.90384319
Median Absolute Deviation (MAD)0.0092775435
Skewness9.9801318
Sum844965.04
Variance25.068119
MonotonicityNot monotonic
2023-05-30T13:29:33.811984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02801972689 4
 
< 0.1%
0.03054321527 3
 
< 0.1%
0.01274189252 3
 
< 0.1%
0.02032779054 3
 
< 0.1%
0.01851027811 3
 
< 0.1%
0.02406238422 3
 
< 0.1%
0.02174377305 3
 
< 0.1%
0.01886675185 3
 
< 0.1%
0.02812341688 3
 
< 0.1%
0.0242542157 3
 
< 0.1%
Other values (925115) 934827
> 99.9%
ValueCountFrequency (%)
3.112790756 × 10-81
< 0.1%
4.284949667 × 10-81
< 0.1%
6.467590399 × 10-81
< 0.1%
6.668673265 × 10-81
< 0.1%
8.972571815 × 10-81
< 0.1%
1.184335368 × 10-71
< 0.1%
1.636929874 × 10-71
< 0.1%
1.936133843 × 10-71
< 0.1%
2.340225221 × 10-71
< 0.1%
2.459462076 × 10-71
< 0.1%
ValueCountFrequency (%)
76.57750471 1
< 0.1%
76.44178416 1
< 0.1%
76.35261379 1
< 0.1%
76.28622045 1
< 0.1%
76.10338234 1
< 0.1%
76.0644503 1
< 0.1%
76.03996532 1
< 0.1%
75.77016058 1
< 0.1%
75.54222839 1
< 0.1%
75.53508479 1
< 0.1%

intended_balcon_amount
Real number (ℝ)

Distinct930349
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5497008
Minimum-15.537329
Maximum112.7025
Zeros0
Zeros (%)0.0%
Negative702701
Negative (%)75.2%
Memory size14.3 MiB
2023-05-30T13:29:33.981919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-15.537329
5-th percentile-1.5820798
Q1-1.1797899
median-0.83456962
Q3-0.069674002
95-th percentile50.450941
Maximum112.7025
Range128.23983
Interquartile range (IQR)1.1101159

Descriptive statistics

Standard deviation20.523497
Coefficient of variation (CV)2.400493
Kurtosis7.2154814
Mean8.5497008
Median Absolute Deviation (MAD)0.4139853
Skewness2.586153
Sum7992756.2
Variance421.21393
MonotonicityNot monotonic
2023-05-30T13:29:34.150308image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.383355992 3
 
< 0.1%
-1.271576718 3
 
< 0.1%
-0.89533763 3
 
< 0.1%
-1.511615383 3
 
< 0.1%
-0.8159297155 3
 
< 0.1%
-0.6402193199 3
 
< 0.1%
-1.302431331 3
 
< 0.1%
-0.5522859925 3
 
< 0.1%
-1.480167178 3
 
< 0.1%
-0.6157640927 3
 
< 0.1%
Other values (930339) 934828
> 99.9%
ValueCountFrequency (%)
-15.5373287 1
< 0.1%
-14.98174304 1
< 0.1%
-14.46186413 1
< 0.1%
-14.24921104 1
< 0.1%
-14.09788792 1
< 0.1%
-14.0649795 1
< 0.1%
-13.50054653 1
< 0.1%
-13.48327333 1
< 0.1%
-13.15491677 1
< 0.1%
-13.10520997 1
< 0.1%
ValueCountFrequency (%)
112.7025044 1
< 0.1%
112.613538 1
< 0.1%
112.4807053 1
< 0.1%
112.4618379 1
< 0.1%
112.3336455 1
< 0.1%
112.3298244 1
< 0.1%
112.1683185 1
< 0.1%
112.1390146 1
< 0.1%
112.0994771 1
< 0.1%
112.0909752 1
< 0.1%

payment_type
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
AB
373204 
AA
233076 
AC
230914 
AD
97443 
AE
 
221

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1869716
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAB
2nd rowAA
3rd rowAA
4th rowAB
5th rowAC

Common Values

ValueCountFrequency (%)
AB 373204
39.9%
AA 233076
24.9%
AC 230914
24.7%
AD 97443
 
10.4%
AE 221
 
< 0.1%

Length

2023-05-30T13:29:34.289511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-30T13:29:34.432969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
ab 373204
39.9%
aa 233076
24.9%
ac 230914
24.7%
ad 97443
 
10.4%
ae 221
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 1167934
62.5%
B 373204
 
20.0%
C 230914
 
12.4%
D 97443
 
5.2%
E 221
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1869716
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1167934
62.5%
B 373204
 
20.0%
C 230914
 
12.4%
D 97443
 
5.2%
E 221
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1869716
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1167934
62.5%
B 373204
 
20.0%
C 230914
 
12.4%
D 97443
 
5.2%
E 221
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1869716
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1167934
62.5%
B 373204
 
20.0%
C 230914
 
12.4%
D 97443
 
5.2%
E 221
 
< 0.1%

zip_count_4w
Real number (ℝ)

Distinct6238
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1517.628
Minimum1
Maximum6650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2023-05-30T13:29:34.584766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile492
Q1886
median1208
Q31844
95-th percentile3543
Maximum6650
Range6649
Interquartile range (IQR)958

Descriptive statistics

Standard deviation965.05244
Coefficient of variation (CV)0.63589524
Kurtosis2.4506194
Mean1517.628
Median Absolute Deviation (MAD)413
Skewness1.5410731
Sum1.4187667 × 109
Variance931326.21
MonotonicityNot monotonic
2023-05-30T13:29:34.735578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1023 832
 
0.1%
924 828
 
0.1%
1033 822
 
0.1%
1014 819
 
0.1%
1021 817
 
0.1%
1048 808
 
0.1%
1062 808
 
0.1%
1054 801
 
0.1%
1000 801
 
0.1%
1080 798
 
0.1%
Other values (6228) 926724
99.1%
ValueCountFrequency (%)
1 3
 
< 0.1%
2 5
< 0.1%
3 6
< 0.1%
4 3
 
< 0.1%
5 7
< 0.1%
6 4
< 0.1%
7 4
< 0.1%
8 7
< 0.1%
9 4
< 0.1%
10 9
< 0.1%
ValueCountFrequency (%)
6650 1
< 0.1%
6593 1
< 0.1%
6557 1
< 0.1%
6553 1
< 0.1%
6526 2
< 0.1%
6513 1
< 0.1%
6511 1
< 0.1%
6506 1
< 0.1%
6501 1
< 0.1%
6496 1
< 0.1%

velocity_6h
Real number (ℝ)

Distinct933695
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5489.967
Minimum-174.10969
Maximum16754.959
Zeros0
Zeros (%)0.0%
Negative41
Negative (%)< 0.1%
Memory size14.3 MiB
2023-05-30T13:29:34.907550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-174.10969
5-th percentile1222.6418
Q13332.844
median5189.1744
Q37368.2571
95-th percentile10877.826
Maximum16754.959
Range16929.069
Interquartile range (IQR)4035.4131

Descriptive statistics

Standard deviation2940.6949
Coefficient of variation (CV)0.53564893
Kurtosis0.14308084
Mean5489.967
Median Absolute Deviation (MAD)1995.2389
Skewness0.60083345
Sum5.1323395 × 109
Variance8647686.6
MonotonicityNot monotonic
2023-05-30T13:29:35.059304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5869.79409 3
 
< 0.1%
4430.639621 2
 
< 0.1%
8686.23003 2
 
< 0.1%
11488.62541 2
 
< 0.1%
4615.96053 2
 
< 0.1%
2667.946788 2
 
< 0.1%
7989.813294 2
 
< 0.1%
9986.869622 2
 
< 0.1%
4547.489877 2
 
< 0.1%
3260.869008 2
 
< 0.1%
Other values (933685) 934837
> 99.9%
ValueCountFrequency (%)
-174.1096908 1
< 0.1%
-155.4307304 1
< 0.1%
-130.456928 1
< 0.1%
-113.0468992 1
< 0.1%
-110.7034762 1
< 0.1%
-106.9782971 1
< 0.1%
-96.51829979 1
< 0.1%
-84.13861148 1
< 0.1%
-77.95925234 1
< 0.1%
-75.12062215 1
< 0.1%
ValueCountFrequency (%)
16754.95902 1
< 0.1%
16754.20092 1
< 0.1%
16715.5654 1
< 0.1%
16701.86995 1
< 0.1%
16573.28576 1
< 0.1%
16558.19839 1
< 0.1%
16554.13314 1
< 0.1%
16528.15786 1
< 0.1%
16517.25703 1
< 0.1%
16515.61382 1
< 0.1%

velocity_24h
Real number (ℝ)

Distinct933964
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4661.0739
Minimum1322.3252
Maximum9539.3565
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2023-05-30T13:29:35.242260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1322.3252
5-th percentile2550.4766
Q13502.6706
median4640.7363
Q35591.9666
95-th percentile7265.9299
Maximum9539.3565
Range8217.0314
Interquartile range (IQR)2089.296

Descriptive statistics

Standard deviation1451.792
Coefficient of variation (CV)0.31147157
Kurtosis-0.19879736
Mean4661.0739
Median Absolute Deviation (MAD)1051.3865
Skewness0.42303094
Sum4.3574422 × 109
Variance2107700
MonotonicityNot monotonic
2023-05-30T13:29:35.396163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4197.278153 3
 
< 0.1%
5594.539998 3
 
< 0.1%
5083.929288 3
 
< 0.1%
6181.031062 2
 
< 0.1%
5059.222766 2
 
< 0.1%
4678.628909 2
 
< 0.1%
2007.025763 2
 
< 0.1%
4869.005134 2
 
< 0.1%
2892.122262 2
 
< 0.1%
5476.417788 2
 
< 0.1%
Other values (933954) 934835
> 99.9%
ValueCountFrequency (%)
1322.325176 1
< 0.1%
1326.681151 1
< 0.1%
1327.867307 1
< 0.1%
1328.306125 1
< 0.1%
1330.702283 1
< 0.1%
1344.745469 1
< 0.1%
1346.622214 1
< 0.1%
1348.830318 1
< 0.1%
1366.255299 1
< 0.1%
1366.485702 1
< 0.1%
ValueCountFrequency (%)
9539.35653 1
< 0.1%
9511.544062 1
< 0.1%
9505.599398 1
< 0.1%
9505.181514 1
< 0.1%
9501.092895 1
< 0.1%
9478.93622 1
< 0.1%
9474.913485 1
< 0.1%
9472.491584 1
< 0.1%
9468.552375 1
< 0.1%
9456.593159 1
< 0.1%

velocity_4w
Real number (ℝ)

Distinct933273
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4733.988
Minimum2870.5916
Maximum7019.201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2023-05-30T13:29:35.553981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2870.5916
5-th percentile3115.3687
Q14238.4434
median4813.3701
Q35331.804
95-th percentile6320.6368
Maximum7019.201
Range4148.6094
Interquartile range (IQR)1093.3606

Descriptive statistics

Standard deviation871.1704
Coefficient of variation (CV)0.18402463
Kurtosis-0.25258232
Mean4733.988
Median Absolute Deviation (MAD)558.33737
Skewness-0.0090538413
Sum4.4256066 × 109
Variance758937.87
MonotonicityNot monotonic
2023-05-30T13:29:35.707710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4237.957845 3
 
< 0.1%
3480.307357 3
 
< 0.1%
4314.579619 3
 
< 0.1%
4358.685619 3
 
< 0.1%
4315.170164 2
 
< 0.1%
4341.056648 2
 
< 0.1%
4468.875565 2
 
< 0.1%
5668.869731 2
 
< 0.1%
5686.995633 2
 
< 0.1%
3046.102441 2
 
< 0.1%
Other values (933263) 934834
> 99.9%
ValueCountFrequency (%)
2870.591613 1
< 0.1%
2896.415063 1
< 0.1%
2918.632222 1
< 0.1%
2919.806878 1
< 0.1%
2920.415951 1
< 0.1%
2921.590392 1
< 0.1%
2922.163499 1
< 0.1%
2922.47631 1
< 0.1%
2926.560489 1
< 0.1%
2928.591401 1
< 0.1%
ValueCountFrequency (%)
7019.20103 1
< 0.1%
6977.711782 1
< 0.1%
6970.521948 1
< 0.1%
6961.908358 1
< 0.1%
6955.383287 1
< 0.1%
6942.150889 1
< 0.1%
6941.960129 1
< 0.1%
6940.29743 1
< 0.1%
6938.88494 1
< 0.1%
6938.768203 1
< 0.1%

bank_branch_count_8w
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2320
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201.15715
Minimum0
Maximum2377
Zeros132507
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2023-05-30T13:29:35.857498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median10
Q331
95-th percentile1486
Maximum2377
Range2377
Interquartile range (IQR)30

Descriptive statistics

Standard deviation473.66974
Coefficient of variation (CV)2.3547249
Kurtosis5.5648219
Mean201.15715
Median Absolute Deviation (MAD)9
Skewness2.5720068
Sum1.8805337 × 108
Variance224363.03
MonotonicityNot monotonic
2023-05-30T13:29:36.008862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 139772
 
15.0%
0 132507
 
14.2%
2 54691
 
5.9%
11 28083
 
3.0%
10 27680
 
3.0%
12 27570
 
2.9%
13 26152
 
2.8%
9 25918
 
2.8%
14 23684
 
2.5%
8 23554
 
2.5%
Other values (2310) 425247
45.5%
ValueCountFrequency (%)
0 132507
14.2%
1 139772
15.0%
2 54691
 
5.9%
3 15136
 
1.6%
4 12269
 
1.3%
5 14775
 
1.6%
6 17857
 
1.9%
7 20843
 
2.2%
8 23554
 
2.5%
9 25918
 
2.8%
ValueCountFrequency (%)
2377 1
< 0.1%
2366 1
< 0.1%
2360 1
< 0.1%
2357 1
< 0.1%
2355 2
< 0.1%
2348 1
< 0.1%
2347 1
< 0.1%
2346 1
< 0.1%
2345 1
< 0.1%
2342 1
< 0.1%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
CA
640367 
CB
126341 
CC
83231 
CF
 
41970
CD
 
24965
Other values (2)
 
17984

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1869716
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCC
2nd rowCA
3rd rowCA
4th rowCB
5th rowCA

Common Values

ValueCountFrequency (%)
CA 640367
68.5%
CB 126341
 
13.5%
CC 83231
 
8.9%
CF 41970
 
4.5%
CD 24965
 
2.7%
CE 17523
 
1.9%
CG 461
 
< 0.1%

Length

2023-05-30T13:29:36.147799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-30T13:29:36.289385image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
ca 640367
68.5%
cb 126341
 
13.5%
cc 83231
 
8.9%
cf 41970
 
4.5%
cd 24965
 
2.7%
ce 17523
 
1.9%
cg 461
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
C 1018089
54.5%
A 640367
34.2%
B 126341
 
6.8%
F 41970
 
2.2%
D 24965
 
1.3%
E 17523
 
0.9%
G 461
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1869716
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 1018089
54.5%
A 640367
34.2%
B 126341
 
6.8%
F 41970
 
2.2%
D 24965
 
1.3%
E 17523
 
0.9%
G 461
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1869716
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 1018089
54.5%
A 640367
34.2%
B 126341
 
6.8%
F 41970
 
2.2%
D 24965
 
1.3%
E 17523
 
0.9%
G 461
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1869716
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 1018089
54.5%
A 640367
34.2%
B 126341
 
6.8%
F 41970
 
2.2%
D 24965
 
1.3%
E 17523
 
0.9%
G 461
 
< 0.1%

credit_risk_score
Real number (ℝ)

Distinct544
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.26063
Minimum-177
Maximum388
Zeros410
Zeros (%)< 0.1%
Negative11360
Negative (%)1.2%
Memory size14.3 MiB
2023-05-30T13:29:36.434971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-177
5-th percentile33
Q190
median130
Q3188
95-th percentile267
Maximum388
Range565
Interquartile range (IQR)98

Descriptive statistics

Standard deviation71.438414
Coefficient of variation (CV)0.51298356
Kurtosis-0.031619124
Mean139.26063
Median Absolute Deviation (MAD)48
Skewness0.28066054
Sum1.3018892 × 108
Variance5103.447
MonotonicityNot monotonic
2023-05-30T13:29:36.606467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110 6308
 
0.7%
113 6263
 
0.7%
108 6250
 
0.7%
116 6205
 
0.7%
112 6204
 
0.7%
115 6172
 
0.7%
107 6134
 
0.7%
109 6134
 
0.7%
106 6108
 
0.7%
105 6087
 
0.7%
Other values (534) 872993
93.4%
ValueCountFrequency (%)
-177 1
 
< 0.1%
-164 2
 
< 0.1%
-162 1
 
< 0.1%
-161 2
 
< 0.1%
-160 1
 
< 0.1%
-157 3
< 0.1%
-154 2
 
< 0.1%
-153 1
 
< 0.1%
-152 1
 
< 0.1%
-150 6
< 0.1%
ValueCountFrequency (%)
388 1
 
< 0.1%
387 3
< 0.1%
386 3
< 0.1%
383 2
 
< 0.1%
382 1
 
< 0.1%
380 5
< 0.1%
378 2
 
< 0.1%
377 7
< 0.1%
376 6
< 0.1%
375 4
< 0.1%

housing_status
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
BC
313646 
BB
281601 
BA
201363 
BE
111667 
BD
 
24771
Other values (2)
 
1810

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1869716
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBC
2nd rowBC
3rd rowBC
4th rowBB
5th rowBA

Common Values

ValueCountFrequency (%)
BC 313646
33.6%
BB 281601
30.1%
BA 201363
21.5%
BE 111667
 
11.9%
BD 24771
 
2.6%
BF 1562
 
0.2%
BG 248
 
< 0.1%

Length

2023-05-30T13:29:36.763656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-30T13:29:36.898480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
bc 313646
33.6%
bb 281601
30.1%
ba 201363
21.5%
be 111667
 
11.9%
bd 24771
 
2.6%
bf 1562
 
0.2%
bg 248
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
B 1216459
65.1%
C 313646
 
16.8%
A 201363
 
10.8%
E 111667
 
6.0%
D 24771
 
1.3%
F 1562
 
0.1%
G 248
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1869716
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 1216459
65.1%
C 313646
 
16.8%
A 201363
 
10.8%
E 111667
 
6.0%
D 24771
 
1.3%
F 1562
 
0.1%
G 248
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1869716
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 1216459
65.1%
C 313646
 
16.8%
A 201363
 
10.8%
E 111667
 
6.0%
D 24771
 
1.3%
F 1562
 
0.1%
G 248
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1869716
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 1216459
65.1%
C 313646
 
16.8%
A 201363
 
10.8%
E 111667
 
6.0%
D 24771
 
1.3%
F 1562
 
0.1%
G 248
 
< 0.1%

bank_months_count
Real number (ℝ)

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.138964
Minimum-1
Maximum32
Zeros0
Zeros (%)0.0%
Negative231094
Negative (%)24.7%
Memory size14.3 MiB
2023-05-30T13:29:37.036488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q11
median6
Q325
95-th percentile30
Maximum32
Range33
Interquartile range (IQR)24

Descriptive statistics

Standard deviation12.125446
Coefficient of variation (CV)1.0885613
Kurtosis-1.4591957
Mean11.138964
Median Absolute Deviation (MAD)7
Skewness0.45281102
Sum10413350
Variance147.02644
MonotonicityNot monotonic
2023-05-30T13:29:37.171268image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
-1 231094
24.7%
1 168374
18.0%
28 75917
 
8.1%
15 56244
 
6.0%
30 52676
 
5.6%
31 44200
 
4.7%
10 41176
 
4.4%
25 39608
 
4.2%
5 29903
 
3.2%
20 28990
 
3.1%
Other values (23) 166676
17.8%
ValueCountFrequency (%)
-1 231094
24.7%
1 168374
18.0%
2 24867
 
2.7%
3 7547
 
0.8%
4 4230
 
0.5%
5 29903
 
3.2%
6 17337
 
1.9%
7 746
 
0.1%
8 41
 
< 0.1%
9 5661
 
0.6%
ValueCountFrequency (%)
32 21
 
< 0.1%
31 44200
4.7%
30 52676
5.6%
29 9222
 
1.0%
28 75917
8.1%
27 4206
 
0.4%
26 23087
 
2.5%
25 39608
4.2%
24 1705
 
0.2%
23 242
 
< 0.1%

has_other_cards
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
0.0
701792 
1.0
233066 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2804574
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 701792
75.1%
1.0 233066
 
24.9%

Length

2023-05-30T13:29:37.309731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-30T13:29:37.433806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 701792
75.1%
1.0 233066
 
24.9%

Most occurring characters

ValueCountFrequency (%)
0 1636650
58.4%
. 934858
33.3%
1 233066
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1869716
66.7%
Other Punctuation 934858
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1636650
87.5%
1 233066
 
12.5%
Other Punctuation
ValueCountFrequency (%)
. 934858
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2804574
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1636650
58.4%
. 934858
33.3%
1 233066
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2804574
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1636650
58.4%
. 934858
33.3%
1 233066
 
8.3%

proposed_credit_limit
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean551.63939
Minimum190
Maximum2100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2023-05-30T13:29:37.534277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum190
5-th percentile200
Q1200
median200
Q31000
95-th percentile1500
Maximum2100
Range1910
Interquartile range (IQR)800

Descriptive statistics

Standard deviation506.69847
Coefficient of variation (CV)0.91853207
Kurtosis-0.23764461
Mean551.63939
Median Absolute Deviation (MAD)0
Skewness1.1428178
Sum5.157045 × 108
Variance256743.34
MonotonicityNot monotonic
2023-05-30T13:29:37.663835image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
200 541859
58.0%
1500 152783
 
16.3%
500 131256
 
14.0%
1000 85501
 
9.1%
2000 7473
 
0.8%
990 7213
 
0.8%
510 6804
 
0.7%
490 828
 
0.1%
210 547
 
0.1%
1900 428
 
< 0.1%
Other values (2) 166
 
< 0.1%
ValueCountFrequency (%)
190 110
 
< 0.1%
200 541859
58.0%
210 547
 
0.1%
490 828
 
0.1%
500 131256
 
14.0%
510 6804
 
0.7%
990 7213
 
0.8%
1000 85501
 
9.1%
1500 152783
 
16.3%
1900 428
 
< 0.1%
ValueCountFrequency (%)
2100 56
 
< 0.1%
2000 7473
 
0.8%
1900 428
 
< 0.1%
1500 152783
16.3%
1000 85501
9.1%
990 7213
 
0.8%
510 6804
 
0.7%
500 131256
14.0%
490 828
 
0.1%
210 547
 
0.1%

foreign_request
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
0.0
912508 
1.0
 
22350

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2804574
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 912508
97.6%
1.0 22350
 
2.4%

Length

2023-05-30T13:29:37.811269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-30T13:29:37.947402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 912508
97.6%
1.0 22350
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 1847366
65.9%
. 934858
33.3%
1 22350
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1869716
66.7%
Other Punctuation 934858
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1847366
98.8%
1 22350
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 934858
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2804574
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1847366
65.9%
. 934858
33.3%
1 22350
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2804574
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1847366
65.9%
. 934858
33.3%
1 22350
 
0.8%

source
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
INTERNET
927655 
TELEAPP
 
7203

Length

Max length8
Median length8
Mean length7.9922951
Min length7

Characters and Unicode

Total characters7471661
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINTERNET
2nd rowINTERNET
3rd rowINTERNET
4th rowINTERNET
5th rowINTERNET

Common Values

ValueCountFrequency (%)
INTERNET 927655
99.2%
TELEAPP 7203
 
0.8%

Length

2023-05-30T13:29:38.060983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-30T13:29:38.185513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
internet 927655
99.2%
teleapp 7203
 
0.8%

Most occurring characters

ValueCountFrequency (%)
E 1869716
25.0%
T 1862513
24.9%
N 1855310
24.8%
I 927655
12.4%
R 927655
12.4%
P 14406
 
0.2%
L 7203
 
0.1%
A 7203
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7471661
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 1869716
25.0%
T 1862513
24.9%
N 1855310
24.8%
I 927655
12.4%
R 927655
12.4%
P 14406
 
0.2%
L 7203
 
0.1%
A 7203
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 7471661
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 1869716
25.0%
T 1862513
24.9%
N 1855310
24.8%
I 927655
12.4%
R 927655
12.4%
P 14406
 
0.2%
L 7203
 
0.1%
A 7203
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7471661
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 1869716
25.0%
T 1862513
24.9%
N 1855310
24.8%
I 927655
12.4%
R 927655
12.4%
P 14406
 
0.2%
L 7203
 
0.1%
A 7203
 
0.1%

session_length_in_minutes
Real number (ℝ)

Distinct930312
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8147995
Minimum-1
Maximum85.567848
Zeros0
Zeros (%)0.0%
Negative2109
Negative (%)0.2%
Memory size14.3 MiB
2023-05-30T13:29:38.318988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1.2655275
Q13.1510455
median5.2463658
Q39.365532
95-th percentile22.545114
Maximum85.567848
Range86.567848
Interquartile range (IQR)6.2144864

Descriptive statistics

Standard deviation8.2378159
Coefficient of variation (CV)1.0541302
Kurtosis13.653172
Mean7.8147995
Median Absolute Deviation (MAD)2.7418352
Skewness3.1512544
Sum7305727.8
Variance67.861611
MonotonicityNot monotonic
2023-05-30T13:29:38.978028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 2109
 
0.2%
3.155039553 3
 
< 0.1%
4.091064028 3
 
< 0.1%
4.941366469 3
 
< 0.1%
4.317986256 3
 
< 0.1%
8.993731516 3
 
< 0.1%
5.369191087 3
 
< 0.1%
4.706080005 3
 
< 0.1%
5.771883817 3
 
< 0.1%
4.766375488 3
 
< 0.1%
Other values (930302) 932722
99.8%
ValueCountFrequency (%)
-1 2109
0.2%
4.088611726 × 10-51
 
< 0.1%
0.001224497171 1
 
< 0.1%
0.00149964329 1
 
< 0.1%
0.001894059749 1
 
< 0.1%
0.003472779563 1
 
< 0.1%
0.004286417425 1
 
< 0.1%
0.006187519682 1
 
< 0.1%
0.007119304742 1
 
< 0.1%
0.007424412727 1
 
< 0.1%
ValueCountFrequency (%)
85.5678478 1
< 0.1%
85.16199762 1
< 0.1%
84.17860796 1
< 0.1%
83.37677458 1
< 0.1%
82.69866386 1
< 0.1%
82.34065183 1
< 0.1%
82.30868693 1
< 0.1%
81.95029589 1
< 0.1%
81.89104262 1
< 0.1%
81.83908038 1
< 0.1%

device_os
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
linux
314924 
windows
284514 
other
280698 
macintosh
47003 
x11
 
7719

Length

Max length9
Median length5
Mean length5.7932777
Min length3

Characters and Unicode

Total characters5415892
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowother
2nd rowmacintosh
3rd rowwindows
4th rowwindows
5th rowother

Common Values

ValueCountFrequency (%)
linux 314924
33.7%
windows 284514
30.4%
other 280698
30.0%
macintosh 47003
 
5.0%
x11 7719
 
0.8%

Length

2023-05-30T13:29:39.126173image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-30T13:29:39.276690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
linux 314924
33.7%
windows 284514
30.4%
other 280698
30.0%
macintosh 47003
 
5.0%
x11 7719
 
0.8%

Most occurring characters

ValueCountFrequency (%)
i 646441
11.9%
n 646441
11.9%
o 612215
11.3%
w 569028
10.5%
s 331517
 
6.1%
h 327701
 
6.1%
t 327701
 
6.1%
x 322643
 
6.0%
l 314924
 
5.8%
u 314924
 
5.8%
Other values (7) 1002357
18.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5400454
99.7%
Decimal Number 15438
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 646441
12.0%
n 646441
12.0%
o 612215
11.3%
w 569028
10.5%
s 331517
 
6.1%
h 327701
 
6.1%
t 327701
 
6.1%
x 322643
 
6.0%
l 314924
 
5.8%
u 314924
 
5.8%
Other values (6) 986919
18.3%
Decimal Number
ValueCountFrequency (%)
1 15438
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5400454
99.7%
Common 15438
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 646441
12.0%
n 646441
12.0%
o 612215
11.3%
w 569028
10.5%
s 331517
 
6.1%
h 327701
 
6.1%
t 327701
 
6.1%
x 322643
 
6.0%
l 314924
 
5.8%
u 314924
 
5.8%
Other values (6) 986919
18.3%
Common
ValueCountFrequency (%)
1 15438
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5415892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 646441
11.9%
n 646441
11.9%
o 612215
11.3%
w 569028
10.5%
s 331517
 
6.1%
h 327701
 
6.1%
t 327701
 
6.1%
x 322643
 
6.0%
l 314924
 
5.8%
u 314924
 
5.8%
Other values (7) 1002357
18.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
1.0
519417 
0.0
415441 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2804574
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 519417
55.6%
0.0 415441
44.4%

Length

2023-05-30T13:29:39.396791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-30T13:29:39.517611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 519417
55.6%
0.0 415441
44.4%

Most occurring characters

ValueCountFrequency (%)
0 1350299
48.1%
. 934858
33.3%
1 519417
 
18.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1869716
66.7%
Other Punctuation 934858
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1350299
72.2%
1 519417
 
27.8%
Other Punctuation
ValueCountFrequency (%)
. 934858
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2804574
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1350299
48.1%
. 934858
33.3%
1 519417
 
18.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2804574
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1350299
48.1%
. 934858
33.3%
1 519417
 
18.5%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
0.0
934858 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2804574
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 934858
100.0%

Length

2023-05-30T13:29:39.618279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-30T13:29:39.731229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 934858
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1869716
66.7%
. 934858
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1869716
66.7%
Other Punctuation 934858
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1869716
100.0%
Other Punctuation
ValueCountFrequency (%)
. 934858
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2804574
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1869716
66.7%
. 934858
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2804574
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1869716
66.7%
. 934858
33.3%

month
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6576132
Minimum0
Maximum7
Zeros74895
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2023-05-30T13:29:39.819641image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q35
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1169819
Coefficient of variation (CV)0.5787878
Kurtosis-1.0855253
Mean3.6576132
Median Absolute Deviation (MAD)2
Skewness-0.057178958
Sum3419349
Variance4.4816125
MonotonicityNot monotonic
2023-05-30T13:29:39.927293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 146321
15.7%
5 137826
14.7%
2 133263
14.3%
6 127091
13.6%
4 118198
12.6%
7 98688
10.6%
1 98576
10.5%
0 74895
8.0%
ValueCountFrequency (%)
0 74895
8.0%
1 98576
10.5%
2 133263
14.3%
3 146321
15.7%
4 118198
12.6%
5 137826
14.7%
6 127091
13.6%
7 98688
10.6%
ValueCountFrequency (%)
7 98688
10.6%
6 127091
13.6%
5 137826
14.7%
4 118198
12.6%
3 146321
15.7%
2 133263
14.3%
1 98576
10.5%
0 74895
8.0%

x1
Real number (ℝ)

Distinct934858
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.013953728
Minimum-4.9778644
Maximum6.4348669
Zeros0
Zeros (%)0.0%
Negative464892
Negative (%)49.7%
Memory size14.3 MiB
2023-05-30T13:29:40.078980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-4.9778644
5-th percentile-1.6368629
Q1-0.66806604
median0.0069220021
Q30.68654608
95-th percentile1.6829661
Maximum6.4348669
Range11.412731
Interquartile range (IQR)1.3546121

Descriptive statistics

Standard deviation1.013219
Coefficient of variation (CV)72.612781
Kurtosis0.14897831
Mean0.013953728
Median Absolute Deviation (MAD)0.67730194
Skewness0.065253535
Sum13044.754
Variance1.0266128
MonotonicityNot monotonic
2023-05-30T13:29:40.235414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.2822232989 1
 
< 0.1%
0.7225341106 1
 
< 0.1%
0.09126340671 1
 
< 0.1%
-0.39567329 1
 
< 0.1%
-0.1246639813 1
 
< 0.1%
-0.1216799761 1
 
< 0.1%
0.4070229083 1
 
< 0.1%
-0.4969799753 1
 
< 0.1%
-1.674868027 1
 
< 0.1%
-1.04973528 1
 
< 0.1%
Other values (934848) 934848
> 99.9%
ValueCountFrequency (%)
-4.977864446 1
< 0.1%
-4.913331616 1
< 0.1%
-4.852117653 1
< 0.1%
-4.659952967 1
< 0.1%
-4.602974619 1
< 0.1%
-4.446632241 1
< 0.1%
-4.400445301 1
< 0.1%
-4.371314395 1
< 0.1%
-4.365340579 1
< 0.1%
-4.332117681 1
< 0.1%
ValueCountFrequency (%)
6.434866931 1
< 0.1%
5.938492819 1
< 0.1%
5.647253063 1
< 0.1%
5.605919708 1
< 0.1%
5.518075554 1
< 0.1%
5.497664426 1
< 0.1%
5.481411648 1
< 0.1%
5.35703142 1
< 0.1%
5.335605496 1
< 0.1%
5.187241498 1
< 0.1%

x2
Real number (ℝ)

Distinct934858
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.011317419
Minimum-4.8464137
Maximum6.5424922
Zeros0
Zeros (%)0.0%
Negative465274
Negative (%)49.8%
Memory size14.3 MiB
2023-05-30T13:29:40.394192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-4.8464137
5-th percentile-1.6391733
Q1-0.67028542
median0.0056589419
Q30.68371808
95-th percentile1.6790625
Maximum6.5424922
Range11.388906
Interquartile range (IQR)1.3540035

Descriptive statistics

Standard deviation1.012477
Coefficient of variation (CV)89.461826
Kurtosis0.14895211
Mean0.011317419
Median Absolute Deviation (MAD)0.67698331
Skewness0.065045791
Sum10580.18
Variance1.0251096
MonotonicityNot monotonic
2023-05-30T13:29:40.548720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1371216331 1
 
< 0.1%
-0.674844564 1
 
< 0.1%
-0.2337829497 1
 
< 0.1%
0.577778861 1
 
< 0.1%
0.268263762 1
 
< 0.1%
-0.3774879003 1
 
< 0.1%
-1.629810455 1
 
< 0.1%
0.1262583252 1
 
< 0.1%
-0.06540750097 1
 
< 0.1%
-0.9035089504 1
 
< 0.1%
Other values (934848) 934848
> 99.9%
ValueCountFrequency (%)
-4.846413733 1
< 0.1%
-4.783369547 1
< 0.1%
-4.618998589 1
< 0.1%
-4.471665368 1
< 0.1%
-4.433939506 1
< 0.1%
-4.426691766 1
< 0.1%
-4.3132024 1
< 0.1%
-4.302575372 1
< 0.1%
-4.296208593 1
< 0.1%
-4.28748851 1
< 0.1%
ValueCountFrequency (%)
6.542492182 1
< 0.1%
6.284777011 1
< 0.1%
6.028632545 1
< 0.1%
5.951461793 1
< 0.1%
5.640478546 1
< 0.1%
5.487140277 1
< 0.1%
5.460212461 1
< 0.1%
5.34742463 1
< 0.1%
5.33297376 1
< 0.1%
5.309443306 1
< 0.1%

name_email_similarity
Real number (ℝ)

Distinct872778
Distinct (%)99.9%
Missing61087
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean0.48771499
Minimum5.0247067 × 10-8
Maximum0.99999996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2023-05-30T13:29:40.715929image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum5.0247067 × 10-8
5-th percentile0.067003863
Q10.21468263
median0.48603337
Q30.75462222
95-th percentile0.91665107
Maximum0.99999996
Range0.99999991
Interquartile range (IQR)0.53993959

Descriptive statistics

Standard deviation0.29142273
Coefficient of variation (CV)0.59752671
Kurtosis-1.3088828
Mean0.48771499
Median Absolute Deviation (MAD)0.27010353
Skewness0.070488446
Sum426151.21
Variance0.084927208
MonotonicityNot monotonic
2023-05-30T13:29:40.878374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6543885557 2
 
< 0.1%
0.227845696 2
 
< 0.1%
0.7587758738 2
 
< 0.1%
0.09967890275 2
 
< 0.1%
0.1921911384 2
 
< 0.1%
0.6623782788 2
 
< 0.1%
0.2434066482 2
 
< 0.1%
0.6502823952 2
 
< 0.1%
0.4891073054 2
 
< 0.1%
0.8054028081 2
 
< 0.1%
Other values (872768) 873751
93.5%
(Missing) 61087
 
6.5%
ValueCountFrequency (%)
5.024706719 × 10-81
< 0.1%
7.898993833 × 10-71
< 0.1%
6.11675322 × 10-61
< 0.1%
8.85821685 × 10-61
< 0.1%
1.887405922 × 10-51
< 0.1%
2.022443363 × 10-51
< 0.1%
2.116087418 × 10-51
< 0.1%
2.539521405 × 10-51
< 0.1%
3.198422688 × 10-51
< 0.1%
3.548570023 × 10-51
< 0.1%
ValueCountFrequency (%)
0.9999999633 1
< 0.1%
0.9999998641 1
< 0.1%
0.9999996731 1
< 0.1%
0.9999990265 1
< 0.1%
0.9999984639 1
< 0.1%
0.9999982377 1
< 0.1%
0.9999981359 1
< 0.1%
0.9999977445 1
< 0.1%
0.9999976263 1
< 0.1%
0.9999974194 1
< 0.1%

date_of_birth_distinct_emails_4w
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)< 0.1%
Missing61087
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean7.7732907
Minimum0
Maximum39
Zeros3174
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2023-05-30T13:29:41.042027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median7
Q311
95-th percentile17
Maximum39
Range39
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.8153014
Coefficient of variation (CV)0.61946756
Kurtosis1.0324455
Mean7.7732907
Median Absolute Deviation (MAD)3
Skewness0.99832431
Sum6792076
Variance23.187128
MonotonicityNot monotonic
2023-05-30T13:29:41.185349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
5 94762
10.1%
6 84497
 
9.0%
4 80098
 
8.6%
7 75965
 
8.1%
8 64667
 
6.9%
3 62971
 
6.7%
2 62764
 
6.7%
9 48279
 
5.2%
11 47740
 
5.1%
10 40536
 
4.3%
Other values (30) 211492
22.6%
(Missing) 61087
 
6.5%
ValueCountFrequency (%)
0 3174
 
0.3%
1 30406
 
3.3%
2 62764
6.7%
3 62971
6.7%
4 80098
8.6%
5 94762
10.1%
6 84497
9.0%
7 75965
8.1%
8 64667
6.9%
9 48279
5.2%
ValueCountFrequency (%)
39 1
 
< 0.1%
38 4
 
< 0.1%
37 8
 
< 0.1%
36 12
 
< 0.1%
35 40
 
< 0.1%
34 49
 
< 0.1%
33 63
 
< 0.1%
32 101
< 0.1%
31 134
< 0.1%
30 196
< 0.1%

email_is_free
Categorical

Distinct2
Distinct (%)< 0.1%
Missing61087
Missing (%)6.5%
Memory size14.3 MiB
1.0
453162 
0.0
420609 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2621313
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 453162
48.5%
0.0 420609
45.0%
(Missing) 61087
 
6.5%

Length

2023-05-30T13:29:41.335999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-30T13:29:41.467332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 453162
51.9%
0.0 420609
48.1%

Most occurring characters

ValueCountFrequency (%)
0 1294380
49.4%
. 873771
33.3%
1 453162
 
17.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1747542
66.7%
Other Punctuation 873771
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1294380
74.1%
1 453162
 
25.9%
Other Punctuation
ValueCountFrequency (%)
. 873771
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2621313
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1294380
49.4%
. 873771
33.3%
1 453162
 
17.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2621313
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1294380
49.4%
. 873771
33.3%
1 453162
 
17.3%

device_distinct_emails_8w
Categorical

IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing61087
Missing (%)6.5%
Memory size14.3 MiB
1.0
842074 
2.0
 
25681
0.0
 
5735
-1.0
 
281

Length

Max length4
Median length3
Mean length3.0003216
Min length3

Characters and Unicode

Total characters2621594
Distinct characters5
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 842074
90.1%
2.0 25681
 
2.7%
0.0 5735
 
0.6%
-1.0 281
 
< 0.1%
(Missing) 61087
 
6.5%

Length

2023-05-30T13:29:41.581532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-30T13:29:41.729508image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 842355
96.4%
2.0 25681
 
2.9%
0.0 5735
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 879506
33.5%
. 873771
33.3%
1 842355
32.1%
2 25681
 
1.0%
- 281
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1747542
66.7%
Other Punctuation 873771
33.3%
Dash Punctuation 281
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 879506
50.3%
1 842355
48.2%
2 25681
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 873771
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 281
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2621594
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 879506
33.5%
. 873771
33.3%
1 842355
32.1%
2 25681
 
1.0%
- 281
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2621594
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 879506
33.5%
. 873771
33.3%
1 842355
32.1%
2 25681
 
1.0%
- 281
 
< 0.1%

phone_home_valid
Categorical

Distinct2
Distinct (%)< 0.1%
Missing61087
Missing (%)6.5%
Memory size14.3 MiB
0.0
442588 
1.0
431183 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2621313
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 442588
47.3%
1.0 431183
46.1%
(Missing) 61087
 
6.5%

Length

2023-05-30T13:29:41.844429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-30T13:29:41.977842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 442588
50.7%
1.0 431183
49.3%

Most occurring characters

ValueCountFrequency (%)
0 1316359
50.2%
. 873771
33.3%
1 431183
 
16.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1747542
66.7%
Other Punctuation 873771
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1316359
75.3%
1 431183
 
24.7%
Other Punctuation
ValueCountFrequency (%)
. 873771
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2621313
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1316359
50.2%
. 873771
33.3%
1 431183
 
16.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2621313
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1316359
50.2%
. 873771
33.3%
1 431183
 
16.4%
Distinct2
Distinct (%)< 0.1%
Missing61087
Missing (%)6.5%
Memory size14.3 MiB
1.0
748761 
0.0
125010 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2621313
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 748761
80.1%
0.0 125010
 
13.4%
(Missing) 61087
 
6.5%

Length

2023-05-30T13:29:42.087181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-30T13:29:42.217802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 748761
85.7%
0.0 125010
 
14.3%

Most occurring characters

ValueCountFrequency (%)
0 998781
38.1%
. 873771
33.3%
1 748761
28.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1747542
66.7%
Other Punctuation 873771
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 998781
57.2%
1 748761
42.8%
Other Punctuation
ValueCountFrequency (%)
. 873771
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2621313
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 998781
38.1%
. 873771
33.3%
1 748761
28.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2621313
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 998781
38.1%
. 873771
33.3%
1 748761
28.6%

Interactions

2023-05-30T13:29:16.432461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:32.967099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:37.932658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:43.401587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:48.558357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:53.623490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:59.091436image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:04.199348image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:09.191580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:14.461533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:20.214337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:25.259243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:30.374649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:35.283767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:40.290678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:45.738215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:51.116609image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:56.286800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:01.138990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:06.224712image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:11.247061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:16.669287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:33.216278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:38.170287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:43.648315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:48.798763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:53.860382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:59.324018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:04.434853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:09.440580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:14.716942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:20.454242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:25.497942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:30.624679image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:35.514738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:40.512611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:46.010500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:51.348421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:56.542466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:01.380929image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:06.444082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:11.489587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:16.917600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:33.462067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:38.433428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:43.899233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:49.053659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:54.107060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:59.579941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:04.684043image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:09.696240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:14.995186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:20.703707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:25.751474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:30.880801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:35.792212image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:40.754144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:46.311185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:51.593365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:56.804742image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:01.620079image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:06.673001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:11.741959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:17.170519image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:33.714221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:38.685963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:44.149081image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:49.291988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:54.354156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:59.835305image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:04.929837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:09.955988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:15.252276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:20.945821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:26.012015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:31.127376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:36.048193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:41.008174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:46.620687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:51.839434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:57.054895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:01.858231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:06.904880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-05-30T13:28:17.747466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-05-30T13:28:28.385801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:33.506994image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:38.411853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:43.688856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:49.082247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:54.344276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:59.300354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:04.482925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:09.304515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:14.573079image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:20.021806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:36.254562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:41.652505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:46.836774image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:51.920086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:56.961083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:02.466773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:07.505983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:12.602166image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:18.004806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:23.581444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:28.646614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:33.734449image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:38.635719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:43.917319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:49.312234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:54.599377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:59.521358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:04.699294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:09.554850image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:14.820928image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:20.291822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:36.497304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:41.902592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:47.086014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:52.163916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:57.206634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:02.731121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:07.752607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:12.865129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:18.260855image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:23.831064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:28.897024image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:33.958831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:38.884062image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:44.192442image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:49.575328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:54.855383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:59.748730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:04.921242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:09.810383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:15.057597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:20.553018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:36.731183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:42.137167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:47.321976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:52.390896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:57.436408image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:02.979615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:07.979059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:13.142130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:18.505919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:24.058614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:29.127011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:34.173346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:39.123968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:44.436569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:49.811962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:55.094983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:59.963047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:05.129356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:10.034278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:15.280281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:20.816872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:36.968879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:42.393156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:47.569609image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:52.639477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:57.687607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:03.223592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:08.222763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:13.393685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:18.758623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:24.300785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:29.368028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:34.395893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:39.379418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:44.696748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:50.068877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:55.318232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:00.189373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:05.349298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:10.282741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:15.513650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:21.076298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:37.200074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:42.631371image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:47.807603image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:52.894302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:57.923543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:03.458311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:08.451758image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:13.653820image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:19.456198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:24.534174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:29.597162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:34.613634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:39.605007image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:44.929400image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:50.324429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:55.551557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:00.413280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:05.557203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:10.515553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:15.741126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:21.358521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:37.440326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:42.881951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:48.050642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:53.126650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:58.162998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:03.695741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:08.684824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:13.924490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:19.704785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:24.776606image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:29.836843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:34.822720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:39.824318image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:45.196039image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:50.586228image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:55.793776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:00.631325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:05.776493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:10.750082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:15.959448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:21.631927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:37.682531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:43.128309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:48.302692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:53.379998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:27:58.845727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:03.942116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:08.933868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:14.193017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:19.955085image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:25.011426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:30.112698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:35.048633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:40.053257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:45.451115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:50.854997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:28:56.033346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:00.887168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:05.995925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:10.993560image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-30T13:29:16.183421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-05-30T13:29:42.376674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
idincomeprev_address_months_countcurrent_address_months_countcustomer_agedays_since_requestintended_balcon_amountzip_count_4wvelocity_6hvelocity_24hvelocity_4wbank_branch_count_8wcredit_risk_scorebank_months_countproposed_credit_limitsession_length_in_minutesmonthx1x2name_email_similaritydate_of_birth_distinct_emails_4wfraud_boolpayment_typeemployment_statushousing_statushas_other_cardsforeign_requestsourcedevice_oskeep_alive_sessionemail_is_freedevice_distinct_emails_8wphone_home_validphone_mobile_valid
id1.0000.001-0.0010.0000.001-0.000-0.000-0.000-0.001-0.001-0.000-0.0000.0010.0010.001-0.0000.001-0.0000.0010.000-0.0010.0020.0000.0010.0010.0010.0000.0000.0000.0000.0000.0010.0040.002
income0.0011.0000.009-0.0190.087-0.0280.108-0.079-0.100-0.114-0.1220.0130.1860.0060.142-0.0840.1310.0030.003-0.033-0.0380.0520.0300.0610.0850.0880.0130.0140.0400.0400.0250.0160.0130.037
prev_address_months_count-0.0010.0091.000-0.611-0.1640.015-0.033-0.0690.0060.0250.023-0.070-0.055-0.076-0.0580.070-0.014-0.003-0.002-0.0320.1530.0240.0420.0260.0510.0440.0160.0120.0240.0400.0220.0110.0510.023
current_address_months_count0.000-0.019-0.6111.0000.225-0.0190.0930.0560.0180.0050.0140.0460.1310.0750.155-0.039-0.0130.0010.0010.046-0.2310.0500.0570.0630.1800.0750.0230.0160.0530.0640.0890.0180.1420.111
customer_age0.0010.087-0.1640.2251.0000.039-0.015-0.0240.0020.008-0.0060.0600.1400.0470.1270.0540.013-0.006-0.005-0.044-0.5600.0330.0490.1660.1800.1160.0110.0320.0930.0670.0410.0370.2530.174
days_since_request-0.000-0.0280.015-0.0190.0391.000-0.043-0.0340.0710.0540.0310.019-0.100-0.002-0.0840.059-0.031-0.001-0.002-0.033-0.0570.0070.0720.0140.0290.0550.0020.0180.0150.0020.0180.0120.0530.015
intended_balcon_amount-0.0000.108-0.0330.093-0.015-0.0431.0000.0060.0340.0650.0670.1600.0290.1960.1000.045-0.051-0.000-0.0030.061-0.0050.0310.4350.0390.0760.1340.0120.0150.0560.0290.0210.0250.0260.063
zip_count_4w-0.000-0.079-0.0690.056-0.024-0.0340.0061.0000.1290.1980.2690.012-0.0920.048-0.0250.048-0.268-0.0000.0030.0180.0980.0150.0530.0370.0360.0560.0240.0160.0200.0400.0270.0190.0880.036
velocity_6h-0.001-0.1000.0060.0180.0020.0710.0340.1291.0000.4560.3840.017-0.1430.014-0.0580.065-0.3960.0000.0010.0230.0680.0160.0680.0370.0420.0430.0100.0160.0390.0350.0450.0270.0530.031
velocity_24h-0.001-0.1140.0250.0050.0080.0540.0650.1980.4561.0000.5280.037-0.1440.017-0.0200.087-0.539-0.0000.0000.0260.0950.0120.0610.0400.0380.0590.0200.0190.0270.0510.0480.0370.0560.041
velocity_4w-0.000-0.1220.0230.014-0.0060.0310.0670.2690.3840.5281.0000.034-0.1730.0200.0040.112-0.837-0.000-0.0000.0540.1520.0240.0730.0440.0610.0830.0330.0260.0510.0980.0520.0520.0930.070
bank_branch_count_8w-0.0000.013-0.0700.0460.0600.0190.1600.0120.0170.0370.0341.000-0.0310.5020.0060.009-0.038-0.004-0.002-0.023-0.0290.0210.1430.0210.0280.0640.0070.0190.0270.0120.0110.0120.0750.024
credit_risk_score0.0010.186-0.0550.1310.140-0.1000.029-0.092-0.143-0.144-0.173-0.0311.000-0.0440.662-0.0420.1670.0040.0040.060-0.1140.0780.0470.0640.1420.1390.0150.0210.0710.0410.0340.0380.0190.034
bank_months_count0.0010.006-0.0760.0750.047-0.0020.1960.0480.0140.0170.0200.502-0.0441.000-0.0070.022-0.016-0.0030.000-0.018-0.0340.0270.2930.0570.0430.0460.0120.0370.0360.0310.0190.0230.0810.031
proposed_credit_limit0.0010.142-0.0580.1550.127-0.0840.100-0.025-0.058-0.0200.0040.0060.662-0.0071.000-0.004-0.0110.0040.0020.082-0.0540.1000.0580.0630.1450.1290.0270.0170.0650.0610.0460.0280.0470.030
session_length_in_minutes-0.000-0.0840.070-0.0390.0540.0590.0450.0480.0650.0870.1120.009-0.0420.022-0.0041.000-0.1040.001-0.0010.022-0.0530.0160.0290.0330.0290.1210.0100.0420.0340.0490.0440.0500.0380.017
month0.0010.131-0.014-0.0130.013-0.031-0.051-0.268-0.396-0.539-0.837-0.0380.167-0.016-0.011-0.1041.000-0.001-0.000-0.042-0.1630.0260.0760.0450.0660.0870.0280.0260.0540.1110.0660.0520.1010.075
x1-0.0000.003-0.0030.001-0.006-0.001-0.000-0.0000.000-0.000-0.000-0.0040.004-0.0030.0040.001-0.0011.0000.015-0.0050.0010.2800.0070.0040.0090.0120.0010.0000.0090.0140.0090.0040.0180.002
x20.0010.003-0.0020.001-0.005-0.002-0.0030.0030.0010.000-0.000-0.0020.0040.0000.002-0.001-0.0000.0151.000-0.0020.0020.2830.0060.0050.0100.0120.0050.0000.0090.0120.0090.0050.0190.003
name_email_similarity0.000-0.033-0.0320.046-0.044-0.0330.0610.0180.0230.0260.054-0.0230.060-0.0180.0820.022-0.042-0.005-0.0021.0000.0420.0390.0690.0420.0450.0380.0250.0120.0430.0440.0740.0200.0300.043
date_of_birth_distinct_emails_4w-0.001-0.0380.153-0.231-0.560-0.057-0.0050.0980.0680.0950.152-0.029-0.114-0.034-0.054-0.053-0.1630.0010.0020.0421.0000.0300.0770.1520.0970.0580.0300.0290.0550.0600.0530.0380.2220.142
fraud_bool0.0020.0520.0240.0500.0330.0070.0310.0150.0160.0120.0240.0210.0780.0270.1000.0160.0260.2800.2830.0390.0301.0000.0410.0330.0980.0380.0170.0030.0730.0490.0280.0400.0460.008
payment_type0.0000.0300.0420.0570.0490.0720.4350.0530.0680.0610.0730.1430.0470.2930.0580.0290.0760.0070.0060.0690.0770.0411.0000.0590.1020.1570.0270.0650.0630.0310.0300.0450.0840.069
employment_status0.0010.0610.0260.0630.1660.0140.0390.0370.0370.0400.0440.0210.0640.0570.0630.0330.0450.0040.0050.0420.1520.0330.0591.0000.1150.0390.0240.0370.0630.0700.0160.0430.1740.177
housing_status0.0010.0850.0510.1800.1800.0290.0760.0360.0420.0380.0610.0280.1420.0430.1450.0290.0660.0090.0100.0450.0970.0980.1020.1151.0000.0730.0280.0220.0790.0660.0880.0250.1170.098
has_other_cards0.0010.0880.0440.0750.1160.0550.1340.0560.0430.0590.0830.0640.1390.0460.1290.1210.0870.0120.0120.0380.0580.0380.1570.0390.0731.0000.0040.0140.0440.0870.0330.0340.1050.017
foreign_request0.0000.0130.0160.0230.0110.0020.0120.0240.0100.0200.0330.0070.0150.0120.0270.0100.0280.0010.0050.0250.0300.0170.0270.0240.0280.0041.0000.0070.0520.0150.0290.0070.0090.009
source0.0000.0140.0120.0160.0320.0180.0150.0160.0160.0190.0260.0190.0210.0370.0170.0420.0260.0000.0000.0120.0290.0030.0650.0370.0220.0140.0071.0000.0840.0860.0010.4210.0110.024
device_os0.0000.0400.0240.0530.0930.0150.0560.0200.0390.0270.0510.0270.0710.0360.0650.0340.0540.0090.0090.0430.0550.0730.0630.0630.0790.0440.0520.0841.0000.0670.1510.0420.0730.094
keep_alive_session0.0000.0400.0400.0640.0670.0020.0290.0400.0350.0510.0980.0120.0410.0310.0610.0490.1110.0140.0120.0440.0600.0490.0310.0700.0660.0870.0150.0860.0671.0000.0280.1190.0420.026
email_is_free0.0000.0250.0220.0890.0410.0180.0210.0270.0450.0480.0520.0110.0340.0190.0460.0440.0660.0090.0090.0740.0530.0280.0300.0160.0880.0330.0290.0010.1510.0281.0000.0090.0130.035
device_distinct_emails_8w0.0010.0160.0110.0180.0370.0120.0250.0190.0270.0370.0520.0120.0380.0230.0280.0500.0520.0040.0050.0200.0380.0400.0450.0430.0250.0340.0070.4210.0420.1190.0091.0000.0140.069
phone_home_valid0.0040.0130.0510.1420.2530.0530.0260.0880.0530.0560.0930.0750.0190.0810.0470.0380.1010.0180.0190.0300.2220.0460.0840.1740.1170.1050.0090.0110.0730.0420.0130.0141.0000.285
phone_mobile_valid0.0020.0370.0230.1110.1740.0150.0630.0360.0310.0410.0700.0240.0340.0310.0300.0170.0750.0020.0030.0430.1420.0080.0690.1770.0980.0170.0090.0240.0940.0260.0350.0690.2851.000

Missing values

2023-05-30T13:29:22.393907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-30T13:29:25.328427image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-30T13:29:29.342453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idfraud_boolincomeprev_address_months_countcurrent_address_months_countcustomer_agedays_since_requestintended_balcon_amountpayment_typezip_count_4wvelocity_6hvelocity_24hvelocity_4wbank_branch_count_8wemployment_statuscredit_risk_scorehousing_statusbank_months_counthas_other_cardsproposed_credit_limitforeign_requestsourcesession_length_in_minutesdevice_oskeep_alive_sessiondevice_fraud_countmonthx1x2name_email_similaritydate_of_birth_distinct_emails_4wemail_is_freedevice_distinct_emails_8wphone_home_validphone_mobile_valid
000.00.2326.020.060.00.022212-0.758101AB647.05944.1308843862.3162705330.682025763.0CC186.0BC30.00.01000.00.0INTERNET19.450933other1.00.03.0-0.282223-0.1371220.3695102.00.02.00.00.0
120.00.9-1.041.050.00.00258817.710636AA815.07784.6672904863.5797323607.3501511.0CA212.0BC31.00.0500.00.0INTERNET3.850864macintosh0.00.04.0-0.0291791.0891660.1664577.01.01.01.01.0
230.00.823.09.050.00.00402618.224113AA1204.06510.9308786593.3315225245.9970311.0CA130.0BC6.00.0200.00.0INTERNET5.182197windows1.00.02.02.1783651.3016960.2502901.00.01.00.01.0
340.00.1-1.030.030.00.011687-1.434154AB1182.04304.8991273738.2665263609.55126112.0CB164.0BB11.00.0500.00.0INTERNET4.346861windows1.00.07.01.4720990.3676750.6190878.00.01.00.01.0
450.00.983.06.050.00.024149-1.323432AC477.05526.0008904576.3548235042.7583371.0CA205.0BA-1.00.01500.00.0INTERNET4.427453other0.00.03.00.330928-1.5052840.3178565.00.01.00.01.0
570.00.2-1.0286.030.00.034601-1.409049AC1553.02222.4167142817.3378714412.5337891.0CA149.0BB-1.01.0200.00.0INTERNET3.502893other0.00.06.0-0.084331-1.3714670.92349812.00.01.01.01.0
680.00.4-1.036.050.00.026100-0.953027AB822.05556.9757063098.6571624313.48097641.0CC169.0BC2.00.0500.00.0INTERNET21.374936windows0.00.05.0-0.2090232.1414730.2328018.01.01.01.01.0
790.00.9-1.015.050.016.815283-0.301502AB1097.06504.4880174092.2665974299.62526210.0CA67.0BB28.00.0200.00.0INTERNET11.340427other1.00.04.01.521160-0.1907160.0893105.00.01.00.01.0
8100.00.7-1.0269.050.00.01705218.113431AA1008.04256.8946392522.3746543104.674613427.0CA113.0BB11.01.0500.00.0INTERNET1.285402linux1.00.07.00.8190860.9959560.4099155.00.01.01.01.0
9110.00.1-1.0111.050.00.006938-1.296074AB1114.08454.9129005111.4264514834.36694910.0CC52.0BB30.00.0500.00.0INTERNET10.770944linux0.00.04.00.2365400.0542390.2925692.01.01.00.01.0
idfraud_boolincomeprev_address_months_countcurrent_address_months_countcustomer_agedays_since_requestintended_balcon_amountpayment_typezip_count_4wvelocity_6hvelocity_24hvelocity_4wbank_branch_count_8wemployment_statuscredit_risk_scorehousing_statusbank_months_counthas_other_cardsproposed_credit_limitforeign_requestsourcesession_length_in_minutesdevice_oskeep_alive_sessiondevice_fraud_countmonthx1x2name_email_similaritydate_of_birth_distinct_emails_4wemail_is_freedevice_distinct_emails_8wphone_home_validphone_mobile_valid
9348489999900.00.9-1.0168.040.00.025489-0.938422AC1018.05114.9095062891.7688983105.1528382.0CC182.0BB-1.00.0500.00.0INTERNET1.460424linux1.00.06.0-0.5467060.9008220.0174035.00.01.01.00.0
9348499999910.00.528.09.020.00.016669-0.507331AB726.03703.9557306567.2163844258.47202217.0CA91.0BE28.00.0200.00.0INTERNET1.239913other1.00.05.00.9549821.8227430.36166913.01.01.00.01.0
9348509999920.00.2-1.090.050.00.038895-0.767108AB1137.05376.6037193213.6464254227.0394881495.0CA121.0BD1.00.0500.00.0INTERNET8.497648linux1.00.05.0-0.1716271.6364720.1733174.01.01.00.01.0
9348519999930.00.265.011.040.00.00842050.591899AC889.05777.4593413402.3644413808.7872471.0CA86.0BC28.00.0200.00.0INTERNET21.886119other1.00.06.0-0.7241160.4612300.4150239.00.01.00.01.0
9348529999940.00.9117.011.020.00.01315437.492554AA4342.06225.6660223452.5660865907.25305714.0CB313.0BC4.00.01500.00.0INTERNET2.585130windows1.00.00.01.312224-1.5925450.13610813.01.01.01.01.0
9348539999951.00.4-1.0115.050.00.016675-1.277662AD1704.07574.0006045401.9862475194.6862840.0CA43.0BB-1.00.0200.00.0INTERNET4.871168other0.00.03.00.430381-0.3919840.5228871.01.01.00.01.0
9348549999960.00.938.08.020.00.00901250.289551AA1728.03581.5528552510.0568364363.0770012.0CA176.0BC29.00.01500.00.0INTERNET13.960900linux1.00.06.00.802064-1.2993590.47585712.01.01.00.01.0
9348559999970.00.7-1.0305.050.00.001224-1.639289AB780.04878.8641923657.6913615205.2146180.0CA309.0BA30.01.01500.00.0INTERNET2.007887windows1.00.06.00.432079-0.0186340.5121733.00.01.01.00.0
9348569999980.00.8-1.036.060.00.031814-1.671697AB2140.0879.6453764436.2033724218.32483316.0CC186.0BC1.01.0200.00.0INTERNET0.999235windows0.00.04.01.6864240.0651820.1383058.00.01.00.01.0
9348579999990.00.8-1.024.020.00.00843730.574625AA997.02567.3114685457.5639654317.7556521190.0CA83.0BC26.00.0200.00.0INTERNET5.295844linux0.00.04.0-0.6890512.6509350.12544914.01.01.00.01.0